EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.

This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:

Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
Intermediate (Computed) Variables
Response Variables
- Computed Response Variables
- Measured Response Variables

EM Variables by Category

A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:

Policy Regarding Use or Management of Ecosystem Resources
Land Surface (or Water Body Bed) Cover, Use or Substrate
Human Demographic Data
Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
Monetary Values

Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.

All EM Descriptors

This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:

EM Identity and Description
EM Modeling Approach
EM Locations, Environments, Ecology
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

EM Descriptors by Modeling Concepts

This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:

Conceptual Model
Modeling Objective
Modeling Context
Potential for Model Linkage
Feasibility of Model Use
Model Certainty
Model Structural Information

Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.

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Comparing:

EM-306
EM-446
EM-682
EM-846
EM-895

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
EM Short Name em.detail.shortNameHelp ?	Urban Temperature, Baltimore, MD, USA	CRPI, St. Croix, USVI	WTP for a beach day, Massachusetts, USA	Indigo bunting abund, Piedmont region, USA	HWB indicator-College degree, Great Lakes, USA
EM Full Name em.detail.fullNameHelp ?	Urban Air Temperature Change, Baltimore, MD, USA	CRPI (Coral Reef Protection Index, St. Croix, USVI	Willingness to pay (WTP) for a beach day, Barnstable, Massachusetts, USA	Indigo bunting abundance, Piedmont ecoregion, USA	Human well being indicator-College degree, Great Lakes waterfront, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	i-Tree \| USDA Forest Service	US EPA	US EPA	None	US EPA
EM Source Document ID	217	335	386	405	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers
Document Author em.detail.documentAuthorHelp ?	Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta	Riffel, S., Scognamillo, D., and L. W. Burger	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick
Document Year em.detail.documentYearHelp ?	2016	2014	2018	2008	None
Document Title em.detail.sourceIdHelp ?	Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Human well-being and natural capital indictors for Great Lakes waterfront revitalization
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed but unpublished (explain in Comment)
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Journal manuscript submitted or in review

Software and Access

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Gordon M. Heisler	Susan H. Yee	Kate K, Mulvaney	Sam Riffell	Ted Angradi
Contact Address	5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Not reported	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804
Contact Email	gheisler@fs.fed.us	yee.susan@epa.gov	Mulvaney.Kate@EPA.gov	sriffell@cfr.msstate.edu	tedangradi@gmail.com

EM Description

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
Summary Description em.detail.summaryDescriptionHelp ?	An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation."	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...An alternative index has been developed specifically for coral reefs, the Coral Reef Protection Index (CRPI), that accounts for the continuity of the reef and distance from shore in addition to reef habitat type (Burke et al., 2008): CRPI = ((Reef type + Reef distribution + Reef distance)/10) x 4 where the scaled magnitude of coastal protection due to each factor ranges from 0 (no protection) to 4 (very high protection; Table 2)."	ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "We used existing studies in a meta-analysis to estimate appropriate benefit transfer values of consumer surplus per beach visit for Barnstable. The studies we include in the model are for beaches across the United States, allowing the metaregression model to be more broadly applicable to other beaches and for values to be adjusted based on appropriate site attributes...To identify relevant studies, we selected 25 studies of beach use and swimming from the Recreation Use Values Database (RUVD), where consumer surplus values are presented as value per day in 2016 dollars...We added beach length and history of closures to contextualize the model for our application by proxying water quality and site quality." Equation 1, page 11, provides the meta-regression.	ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds."	ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Economic value of protecting coastal beach water quality from contamination caused closures.	None reported	None identified
Biophysical Context	One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site.	No additional description provided	Four separate beaches within the community of Barnstable	Conservation Reserve Program lands left to go fallow	Waterfront districts on south Lake Michigan and south lake Erie
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	N/A	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-220 \| Doc-219 \| Doc-218	None	Doc-386 \| Doc-387	Doc-405	Doc-422
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	EM-684 \| EM-685 \| EM-683 \| EM-686	EM-831 \| EM-838 \| EM-839 \| EM-840 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-847	EM-886 \| EM-888 \| EM-889 \| EM-890 \| EM-891 \| EM-893 \| EM-894

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
EM Temporal Extent em.detail.tempExtentHelp ?	May 5-Sept 30 2006	2006-2007, 2010	July 1, 2011 to June 31, 2016	2008	2022
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Hour	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Physiographic or ecological	Physiographic or ecological	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Baltimore, MD	Coastal zone surrounding St. Croix	Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach)	Piedmont Ecoregion	Great Lakes waterfront
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	100-1000 km^2	10-100 ha	100,000-1,000,000 km^2	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	length, for linear feature (e.g., stream mile)	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	10m x 10m	10 m x 10 m	by beach site	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Machine learning

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Yes	Yes	Yes	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	Yes	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R squared \| 0.50 \| unitless	None	R-squared \| 0.486 \| Not reported Adjusted R-squared \| 0.445 \| Not reported Residual Std. Error \| 0.971 \| Not reported F-statistic \| 11.9 \| Not reported AIC \| 276.47 \| Not reported	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	Yes ? Comment: p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables.	Yes	Yes
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-306	EM-446	EM-682	EM-846	EM-895
North America United States Maryland	None	North America United States Massachusetts	Oceania	North America United States Illinois Minnesota Ohio Wisconsin

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

EM-306	EM-446	EM-682	EM-846	EM-895
None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
Centroid Latitude em.detail.ddLatHelp ?	39.28	17.73	41.64	36.23	42.26
Centroid Longitude em.detail.ddLongHelp ?	-76.62	-64.77	-70.29	-81.9	-87.84
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified) \| Created Greenspace \| Atmosphere	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Grasslands	Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland \| Barren \| Tundra \| Ice and Snow \| Atmosphere
Specific Environment Type em.detail.specificEnvTypeHelp ?	Urban landscape and surrounding area	Coral reefs	Saltwater beach	grasslands	Lake Michigan & Lake Erie waterfront
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-306	EM-446	EM-682	EM-846	EM-895
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Community	Not applicable	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-306	EM-446	EM-682	EM-846	EM-895
None Available	None Available	None Available	Species Indigo bunting	None Available

EnviroAtlas URL

EM-306	EM-446	EM-682	EM-846	EM-895
Average Annual Precipitation, Percent Impervious Area	None Available	None Available	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	Dasymetric Allocation of Population, GAP Ecological Systems, Enabling Conditions

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

EM-306	EM-446	EM-682	EM-846	EM-895
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	None

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)

EM-306	EM-446	EM-682	EM-846	EM-895
Atmosphere Atmosphere (1) Air Site Characteristic Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Site Indicator	Grasslands (23) Fauna (4) Birds Stock Indicator	None